JonathanGarro

# script reviews a simplified version of INFORM index data
# and harmonizes natural hazards, vulnerability, and coping capacity
# then normalizes with population from 2023

import pandas as pd

file_path = 'INFORM.xlsx'
xls = pd.ExcelFile(file_path)

ranks_df = pd.read_excel(xls, 'ranks')

JonathanGarro

# Given the paginated results, this script can take a few minutes to run. Be patient!

import requests
import csv

base_url = "https://goadmin.ifrc.org/api/v2/ops-learning/"

def fetch_data(url):
    """
    Fetches data from the URL passed into it.

JonathanGarro

#!/usr/bin/env python3

import requests

iso2_codes = [
	"AF", "AX", "AL", "DZ", "AS", "AD", "AO", "AI", "AQ", "AG", "AR", "AM", "AW", "AU", "AT", "AZ",
	"BS", "BH", "BD", "BB", "BY", "BE", "BZ", "BJ", "BM", "BT", "BO", "BQ", "BA", "BW", "BV", "BR",
	"IO", "BN", "BG", "BF", "BI", "CV", "KH", "CM", "CA", "KY", "CF", "TD", "CL", "CN", "CX", "CC",
	"CO", "KM", "CD", "CG", "CK", "CR", "HR", "CU", "CW", "CY", "CZ", "DK", "DJ", "DM", "DO", "EC",
	"EG", "SV", "GQ", "ER", "EE", "SZ", "ET", "FK", "FO", "FJ", "FI", "FR", "GF", "PF", "TF", "GA",

JonathanGarro

import math
import csv

# script requires requests library
# prompt user in console to auto pip install it
try:
	import requests
except ImportError as e:
	response = input("The required module 'requests' is not installed. Would you like to install it now? (yes/no): ")
	if response.lower() in ['yes', 'y']:

JonathanGarro

#!/usr/bin/env python3

import csv
import requests

def flatten_dict(d, parent_key='', sep='_'):
	"""
	recursively flattens a nested dictionary to handle GO API data structure.

	function takes a dictionary that may contain nested dictionaries 

JonathanGarro

const width = 750;
const height = 750;
const config = {
	speed: 0.01,
	verticalTilt: -12,
	horizontalTilt: 0
};
let locations = [];
const svg = d3.select('svg').attr('width', width).attr('height', height);
const markerGroup = svg.append('g');

JonathanGarro

import csv
import pandas as pd
from geopy.geocoders import OpenCage

# insert your OpenCage API key
geocoder = OpenCage('<YOUR_API_KEY_HERE>')

# load locations with pandas
location_data = 'largest_cities_coordinates.csv'
data = pd.read_csv(location_data)

JonathanGarro

<svg viewBox="100 0 650 650" preserveAspectRatio="none"></svg>
	<script src="https://d3js.org/d3.v4.min.js"></script>
	<script src="https://d3js.org/topojson.v1.min.js"></script>
  <script>
    const width = 750;
    const height = 750;
      const config = {
      speed: 0.01,
      verticalTilt: -12,
      horizontalTilt: 0

JonathanGarro

"""
Update the event ID and insert your token (leaving quotes around the token code). 
Save this script in a specific folder where you want the CSV that it generates to also be saved.
Note that you will need to update the tags_list values when new profile types are added to RRMS.
"""

import requests
import pandas
import csv

JonathanGarro

#!/usr/bin/env python3

import googlemaps
import pandas as pd
import csv

# save this script in the same folder as your CSV file, then update the name of the file below
location_data = 'file_name_here.csv'
data = pd.read_csv(location_data)